from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				42
   __lowerCAmelCase           =				42





class SCREAMING_SNAKE_CASE_		(      nn.Module		):
   __lowerCAmelCase           =				42
   __lowerCAmelCase           =				(16, 32, 96, 256)
   __lowerCAmelCase           =				jnp.floataa







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      nn.Conv(
        self.block_out_channels[0]   ,   kernel_size=(3, 3)   ,   padding=((1, 1), (1, 1))   ,   dtype=self.dtype   ,   )

    UpperCamelCase			  =      []
    for i in range(len(self.block_out_channels				) - 1				):
     UpperCamelCase			  =      self.block_out_channels[i]
     UpperCamelCase			  =      self.block_out_channels[i + 1]
     UpperCamelCase			  =      nn.Conv(
         lowerCamelCase_   ,   kernel_size=(3, 3)   ,   padding=((1, 1), (1, 1))   ,   dtype=self.dtype   ,   )
     blocks.append(lowerCamelCase_				)
     UpperCamelCase			  =      nn.Conv(
         lowerCamelCase_   ,   kernel_size=(3, 3)   ,   strides=(2, 2)   ,   padding=((1, 1), (1, 1))   ,   dtype=self.dtype   ,   )
     blocks.append(lowerCamelCase_				)
    UpperCamelCase			  =      blocks

    UpperCamelCase			  =      nn.Conv(
        self.conditioning_embedding_channels   ,   kernel_size=(3, 3)   ,   padding=((1, 1), (1, 1))   ,   kernel_init=nn.initializers.zeros_init()   ,   bias_init=nn.initializers.zeros_init()   ,   dtype=self.dtype   ,   )







   def __call__(     self						:						int   ,   lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      self.conv_in(lowerCamelCase_				)
    UpperCamelCase			  =      nn.silu(lowerCamelCase_				)

    for block in self.blocks:
     UpperCamelCase			  =      block(lowerCamelCase_				)
     UpperCamelCase			  =      nn.silu(lowerCamelCase_				)

    UpperCamelCase			  =      self.conv_out(lowerCamelCase_				)

    return embedding





@flax_register_to_config
class SCREAMING_SNAKE_CASE_		(      nn.Module  ,     __lowerCAmelCase  ,     __lowerCAmelCase		):
   __lowerCAmelCase           =				32
   __lowerCAmelCase           =				4
   __lowerCAmelCase           =				(
       "CrossAttnDownBlock2D",
       "CrossAttnDownBlock2D",
       "CrossAttnDownBlock2D",
       "DownBlock2D",
   )
   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				(320, 640, 1_280, 1_280)
   __lowerCAmelCase           =				2
   __lowerCAmelCase           =				8
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				1_280
   __lowerCAmelCase           =				0.0
   __lowerCAmelCase           =				False
   __lowerCAmelCase           =				jnp.floataa
   __lowerCAmelCase           =				True
   __lowerCAmelCase           =				0
   __lowerCAmelCase           =				"rgb"
   __lowerCAmelCase           =				(16, 32, 96, 256)







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   lowerCamelCase_						:						jax.random.KeyArray				):





    """simple docstring"""


    UpperCamelCase			  =      (1, self.in_channels, self.sample_size, self.sample_size)
    UpperCamelCase			  =      jnp.zeros(lowerCamelCase_   ,   dtype=jnp.floataa				)
    UpperCamelCase			  =      jnp.ones((1,)   ,   dtype=jnp.intaa				)
    UpperCamelCase			  =      jnp.zeros((1, 1, self.cross_attention_dim)   ,   dtype=jnp.floataa				)
    UpperCamelCase			  =      (1, 3, self.sample_size * 8, self.sample_size * 8)
    UpperCamelCase			  =      jnp.zeros(lowerCamelCase_   ,   dtype=jnp.floataa				)

    UpperCamelCase  ,     UpperCamelCase			  =      jax.random.split(lowerCamelCase_				)
    UpperCamelCase			  =      {"""params""": params_rng, """dropout""": dropout_rng}

    return self.init(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_				)["params"]







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    UpperCamelCase			  =      self.block_out_channels
    UpperCamelCase			  =      block_out_channels[0] * 4

    # If `num_attention_heads` is not defined (which is the case for most models)
    # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
    # The reason for this behavior is to correct for incorrectly named variables that were introduced
    # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
    # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
    # which is why we correct for the naming here.
    UpperCamelCase			  =      self.num_attention_heads or self.attention_head_dim

    # input
    UpperCamelCase			  =      nn.Conv(
        block_out_channels[0]   ,   kernel_size=(3, 3)   ,   strides=(1, 1)   ,   padding=((1, 1), (1, 1))   ,   dtype=self.dtype   ,   )

    # time
    UpperCamelCase			  =      FlaxTimesteps(
        block_out_channels[0]   ,   flip_sin_to_cos=self.flip_sin_to_cos   ,   freq_shift=self.config.freq_shift				)
    UpperCamelCase			  =      FlaxTimestepEmbedding(lowerCamelCase_   ,   dtype=self.dtype				)

    UpperCamelCase			  =      FlaxControlNetConditioningEmbedding(
        conditioning_embedding_channels=block_out_channels[0]   ,   block_out_channels=self.conditioning_embedding_out_channels   ,   )

    UpperCamelCase			  =      self.only_cross_attention
    if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      (only_cross_attention,) * len(self.down_block_types				)

    if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      (num_attention_heads,) * len(self.down_block_types				)

    # down
    UpperCamelCase			  =      []
    UpperCamelCase			  =      []

    UpperCamelCase			  =      block_out_channels[0]

    UpperCamelCase			  =      nn.Conv(
        lowerCamelCase_   ,   kernel_size=(1, 1)   ,   padding="""VALID"""   ,   kernel_init=nn.initializers.zeros_init()   ,   bias_init=nn.initializers.zeros_init()   ,   dtype=self.dtype   ,   )
    controlnet_down_blocks.append(lowerCamelCase_				)

    for i, down_block_type in enumerate(self.down_block_types				):
     UpperCamelCase			  =      output_channel
     UpperCamelCase			  =      block_out_channels[i]
     UpperCamelCase			  =      i == len(lowerCamelCase_				) - 1

     if down_block_type == "CrossAttnDownBlock2D":
      UpperCamelCase			  =      FlaxCrossAttnDownBlockaD(
          in_channels=lowerCamelCase_   ,   out_channels=lowerCamelCase_   ,   dropout=self.dropout   ,   num_layers=self.layers_per_block   ,   num_attention_heads=num_attention_heads[i]   ,   add_downsample=not is_final_block   ,   use_linear_projection=self.use_linear_projection   ,   only_cross_attention=only_cross_attention[i]   ,   dtype=self.dtype   ,   )
     else:
      UpperCamelCase			  =      FlaxDownBlockaD(
          in_channels=lowerCamelCase_   ,   out_channels=lowerCamelCase_   ,   dropout=self.dropout   ,   num_layers=self.layers_per_block   ,   add_downsample=not is_final_block   ,   dtype=self.dtype   ,   )

     down_blocks.append(lowerCamelCase_				)

     for _ in range(self.layers_per_block				):
      UpperCamelCase			  =      nn.Conv(
          lowerCamelCase_   ,   kernel_size=(1, 1)   ,   padding="""VALID"""   ,   kernel_init=nn.initializers.zeros_init()   ,   bias_init=nn.initializers.zeros_init()   ,   dtype=self.dtype   ,   )
      controlnet_down_blocks.append(lowerCamelCase_				)

     if not is_final_block:
      UpperCamelCase			  =      nn.Conv(
          lowerCamelCase_   ,   kernel_size=(1, 1)   ,   padding="""VALID"""   ,   kernel_init=nn.initializers.zeros_init()   ,   bias_init=nn.initializers.zeros_init()   ,   dtype=self.dtype   ,   )
      controlnet_down_blocks.append(lowerCamelCase_				)

    UpperCamelCase			  =      down_blocks
    UpperCamelCase			  =      controlnet_down_blocks

    # mid
    UpperCamelCase			  =      block_out_channels[-1]
    UpperCamelCase			  =      FlaxUNetMidBlockaDCrossAttn(
        in_channels=lowerCamelCase_   ,   dropout=self.dropout   ,   num_attention_heads=num_attention_heads[-1]   ,   use_linear_projection=self.use_linear_projection   ,   dtype=self.dtype   ,   )

    UpperCamelCase			  =      nn.Conv(
        lowerCamelCase_   ,   kernel_size=(1, 1)   ,   padding="""VALID"""   ,   kernel_init=nn.initializers.zeros_init()   ,   bias_init=nn.initializers.zeros_init()   ,   dtype=self.dtype   ,   )







   def __call__(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						Union[str, Any]   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Optional[Any]   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						float = 1.0   ,   lowerCamelCase_						:						bool = True   ,   lowerCamelCase_						:						bool = False   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self.controlnet_conditioning_channel_order
    if channel_order == "bgr":
     UpperCamelCase			  =      jnp.flip(lowerCamelCase_   ,   axis=1				)

    # 1. time
    if not isinstance(lowerCamelCase_   ,   jnp.ndarray				):
     UpperCamelCase			  =      jnp.array([timesteps]   ,   dtype=jnp.intaa				)
    elif isinstance(lowerCamelCase_   ,   jnp.ndarray				) and len(timesteps.shape				) == 0:
     UpperCamelCase			  =      timesteps.astype(dtype=jnp.floataa				)
     UpperCamelCase			  =      jnp.expand_dims(lowerCamelCase_   ,   0				)

    UpperCamelCase			  =      self.time_proj(lowerCamelCase_				)
    UpperCamelCase			  =      self.time_embedding(lowerCamelCase_				)

    # 2. pre-process
    UpperCamelCase			  =      jnp.transpose(lowerCamelCase_   ,   (0, 2, 3, 1)				)
    UpperCamelCase			  =      self.conv_in(lowerCamelCase_				)

    UpperCamelCase			  =      jnp.transpose(lowerCamelCase_   ,   (0, 2, 3, 1)				)
    UpperCamelCase			  =      self.controlnet_cond_embedding(lowerCamelCase_				)
    sample += controlnet_cond

    # 3. down
    UpperCamelCase			  =      (sample,)
    for down_block in self.down_blocks:
     if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
      UpperCamelCase  ,     UpperCamelCase			  =      down_block(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   deterministic=not train				)
     else:
      UpperCamelCase  ,     UpperCamelCase			  =      down_block(lowerCamelCase_   ,   lowerCamelCase_   ,   deterministic=not train				)
     down_block_res_samples += res_samples

    # 4. mid
    UpperCamelCase			  =      self.mid_block(lowerCamelCase_   ,   lowerCamelCase_   ,   lowerCamelCase_   ,   deterministic=not train				)

    # 5. contronet blocks
    UpperCamelCase			  =      ()
    for down_block_res_sample, controlnet_block in zip(lowerCamelCase_   ,   self.controlnet_down_blocks				):
     UpperCamelCase			  =      controlnet_block(lowerCamelCase_				)
     controlnet_down_block_res_samples += (down_block_res_sample,)

    UpperCamelCase			  =      controlnet_down_block_res_samples

    UpperCamelCase			  =      self.controlnet_mid_block(lowerCamelCase_				)

    # 6. scaling
    UpperCamelCase			  =      [sample * conditioning_scale for sample in down_block_res_samples]
    mid_block_res_sample *= conditioning_scale

    if not return_dict:
     return (down_block_res_samples, mid_block_res_sample)

    return FlaxControlNetOutput(
        down_block_res_samples=lowerCamelCase_   ,   mid_block_res_sample=lowerCamelCase_				)

# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_SCREAMING_SNAKE_CASE 		=      """tiny-wmt19-en-ru"""

# Build

# borrowed from a test
_SCREAMING_SNAKE_CASE 		=      [
    """l""",
    """o""",
    """w""",
    """e""",
    """r""",
    """s""",
    """t""",
    """i""",
    """d""",
    """n""",
    """w</w>""",
    """r</w>""",
    """t</w>""",
    """lo""",
    """low""",
    """er</w>""",
    """low</w>""",
    """lowest</w>""",
    """newer</w>""",
    """wider</w>""",
    """<unk>""",
]
_SCREAMING_SNAKE_CASE 		=      dict(zip(vocab, range(len(vocab))))
_SCREAMING_SNAKE_CASE 		=      ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     _SCREAMING_SNAKE_CASE 		=      Path(tmpdirname)
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     _SCREAMING_SNAKE_CASE 		=      build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     _SCREAMING_SNAKE_CASE 		=      FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

_SCREAMING_SNAKE_CASE 		=      FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=1_0_0_0,
    tgt_vocab_size=1_0_0_0,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_SCREAMING_SNAKE_CASE 		=      FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
_SCREAMING_SNAKE_CASE 		=      tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_SCREAMING_SNAKE_CASE 		=      tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
